论文标题
神经解码的深度学习方法：从CNN到LSTM和SPIKES再到fMRI
Deep learning approaches for neural decoding: from CNNs to LSTMs and spikes to fMRI
论文作者
论文摘要
直接来自神经信号的解码行为，感知或认知状态在脑部计算机界面研究中应用以及对系统神经科学的影响。在过去的十年中，深度学习已成为许多机器学习任务的最新方法，从语音识别到图像细分。深层网络在其他领域的成功导致了神经科学中的新应用。在本文中，我们回顾了神经解码的深度学习方法。我们描述了用于从峰值到脑电图的神经记录方式中提取有用特征的体系结构。此外，我们探讨了如何利用深度学习来预测包括运动，语音和视觉的共同输出，重点是如何将预审慎的深层网络作为先验，以构成复杂的解码目标，例如声音语音或图像。深度学习已被证明是提高各种任务中神经解码的准确性和灵活性的有用工具，我们指出了未来科学发展的领域。
Decoding behavior, perception, or cognitive state directly from neural signals has applications in brain-computer interface research as well as implications for systems neuroscience. In the last decade, deep learning has become the state-of-the-art method in many machine learning tasks ranging from speech recognition to image segmentation. The success of deep networks in other domains has led to a new wave of applications in neuroscience. In this article, we review deep learning approaches to neural decoding. We describe the architectures used for extracting useful features from neural recording modalities ranging from spikes to EEG. Furthermore, we explore how deep learning has been leveraged to predict common outputs including movement, speech, and vision, with a focus on how pretrained deep networks can be incorporated as priors for complex decoding targets like acoustic speech or images. Deep learning has been shown to be a useful tool for improving the accuracy and flexibility of neural decoding across a wide range of tasks, and we point out areas for future scientific development.
